Evolution of network structure by temporal learning

Abstract

We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution.

revised manuscript in communication

References in corpus (4)

• Emergence of scaling in random networks
• Spectral Properties and Synchronization in Coupled Map Lattices
• Evolving networks with distance preferences
• Scale free networks from a Hamiltonian dynamics

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• Self-Organization of Diverse Directional Hierarchical Networks in Simple Coupled Maps with Connection Changes
• The rise and fall of hubs in Self-Organized Critical learning networks